{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8b6f4de-3415-4c10-8a26-ca1ee042d143",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from arch.unitroot import ADF   # pip install arch\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import style\n",
    "style.use('ggplot')\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.tsa.api as smt\n",
    "from statsmodels.tsa.stattools import adfuller \n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox \n",
    "from statsmodels.graphics.api import qqplot\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x) \n",
    "np.set_printoptions(precision=5, suppress=True) \n",
    "\"\"\"中文显示问题\"\"\"\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb0f6bbe-1d53-4418-a15d-31c30091d497",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/train.csv', header=0, encoding='utf-8')\n",
    "df_time_col = data[['时间']]\n",
    "cols = list(data.columns)\n",
    "cols.remove('时间')\n",
    "df = data[cols]\n",
    "# scaler = StandardScaler()\n",
    "# scaler = MinMaxScaler()\n",
    "# df_s = pd.DataFrame(data=scaler.fit_transform(df.values), columns=df.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca7b4b91-1b67-466d-b040-5197adf33406",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model 1    ARIMA模型\n",
    "col_1a = '总理赔金额'\n",
    "col_1b = '总出险数量'\n",
    "df_1a = df[['总理赔金额']]\n",
    "df_1b = df[['总出险数量']]\n",
    "\n",
    "scaler_1a = MinMaxScaler()\n",
    "df_1a = pd.DataFrame(data=scaler_1a.fit_transform(df_1a.values), columns=df_1a.columns)\n",
    "\n",
    "scaler_1b = MinMaxScaler()\n",
    "df_1b = pd.DataFrame(data=scaler_1b.fit_transform(df_1b.values), columns=df_1b.columns)\n",
    "# df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d13e2d0f-062a-4f4d-bc1d-47cc42b322ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001CB0BC64A60>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001CB0BE7FA60>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001CB0B6887C0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#平稳性检验\n",
    "# 时序图\n",
    "# 变量：总理赔金额\n",
    "df_1a[\"diff1\"] = df_1a[col_1a].diff(1).dropna()\n",
    "df_1a[\"diff2\"] = df_1a[\"diff1\"].diff(1).dropna()\n",
    "data1 = df_1a.loc[:,[col_1a,\"diff1\",\"diff2\"]]\n",
    "data1.plot(subplots=True, figsize=(18, 12),title=\"总理赔金额差分图\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "afe9f151-9aae-4b11-bc3f-03f0177b8dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000018EE6D42B20>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000018EE5E69A60>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000018EE5E492E0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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35PPbinqlwmw7yRytVJCk1jXpn0vfOVYrAACA8jA6nFT36bgq/Db19yZ0nlWbwLJFqIBF6z0bl9NlqLFl/gKNcwUq7UW9UmE6lPtQwee3q7beofPn4qT0AABg2UslLR19LSxvhU33PRhQbb1D774ZKYnW4gAyR6iARYlFTQ1cSKhljUt2+/wFGufyV9o0PWXKNIvz5Hp6KiWHU3K5b/69ZKJ1jUvhkKnREf6YAgCA5e3k8aimp0xt3+WV02nojrt8stkNvXE4rFSqON8DAsgeoQIWpe9cXJYprb7J1ocZgUq7LOv9FQHFJt1O0i7DyG2o0NTqlMMhlv4BAIBlbexSUmdOxrSqzaW6hvQqVq/Ppjvu8mlyPKUTx6IFHiGAXCNUQNYsy1LP2biqa+0KrFhYYUN/5eW2kkW6/G06h+0k53I4DDW3utR/Pk4FZAAAsCylUpaOvhqWx2Noy+3eK/6vodmptetd6j4V02A/rbaB5YRQAVkbHUlpeurmBRrn8lemw4epieJbqWCaliLTpioC+XlatK51KZWUBvpYrQAgdxJxU/3n4ywpBlBwpzujCk2a2r7LJ6fz2lWfm7d7VVll09FXw4pGiu+9IIDsECoga71nY3I4peZVCw8VHA5DXp9RlCsVItOmLCu3RRrnqg7aVeG3sQUCQM5MTaZ08P+E9MahsF78wRSf/gEomPHRpN47EVPrGpfqm+Yv3m23G9qxu0KppKW3fhKWVaQ1tgBkhlABWUl/MpbQylUuORyZ1R8IrLBrqgjbSs7UefD5F7aVI1OGYah1rUuXhlOaDhVfqAKgtAz2J/Ryx5QSCUvbdnhlGNKrB6d15KVQUQa3AJYvM2Xp7VfDcrkNbbnDc8NjA5V2bdvh1chQUu91xZZohADyiVABWenrSchMKaOtDzP8AbtCU6miS6enp9Khgj9P2x8kqWWNSzIo2Agge5Zl6fSJqF49OK0Kv133/UxAa9e7df/PBrTldo9GR5L68Q+m1Hk0ogQ1XAAsgfe6YpqcMHXbTp9crpu/j2pd61Jzq1Mn341qbCS5BCMEkE+ECsiYZVnqPRtTZZVdVTWOjG8fWGGTmZLC4eJarTAdSsnuyH07ybm8PpvqGhzprhkWb/YBZCZ5eclw17Gomlc5tecBv3wV6T/lNpuhdRs9euDnKtW6xqUzJ2P6x+9P6nx3jNcbAHkzOZ7Sqc6oVq5yqnHl/NsermYYhm7b6ZPHZ9MbPwkrEec1CihlhArI2MRYSpPjplavy3yVgvR+scZQkW2ByFc7yau1rnUpErY0MkQyD2DhImFTh34U0oXehDbd5tGOu33zbj9ze2za/gGf7vuZdOBw9NWIXu4IaewSrzkoD6GplDrfjmhwIFLooSx7ppnu9uB0Gtq6w3vzG8zhdBm6826fomFTx14PE34CJYxQARnrOROXzS6tzKBA41wzbSWnimzPb77aSV6tcaVTTqfBFggACzY6nNRL/3tK06GUPnBfhdZv9tw0AK2qceien/br9rt8ioRNvdwR0ltHpqm4jmXJsiyNDif12svT+sfvT+lMV0wv/Z9BpZKcqObTmZMxTYyldOudXrndmb+Hqg46tHGbR/3nE7wvAkpY5mvXUdaSCUsXeuNqbnXK6cruE32Xyya3xyiqlQqmaSk8baqpZWHL9hbDbjfUvMqp8+fiSsStrH+OAMpDz5mY3nkzIp/Ppl33+RWoXHgxWcMw1LrGpaaVTp0+EdXZkzFd7Etow1aP1q53y2bn9QelzTQtXbyQ0JmumMZHU3K6DK3f4lZghV1vHg7rzMmYNmy9ceFAZGdqMqVT70bV1OJUc2t2HzRJ0i2b3BoZTOrdNyOqDjoyeo0DUBxYqYCM9J+PK5WUVre5F3U/gUq7piaKZ6VCJGzKMvPXTvJqq9a6ZKbSP08AmI9pWnrnjbCOvR5RsN6he38ms0BhLofT0ObbvLr/ZwOqrXeo8+2ofvwCLShRupIJS2dPxfSP35/SG4fCisfTXVDa/0mlNt3q1cpVLq1eV6H3TkQVKbIaTgt1aTip0yeiMoussLUkWWa624PdYejWOzPb9nA1w2bojrt9sjsMvXl4WqlU8X2/AG6MUAEZ6T0bl7/Spurg4lJkf6Ut3QGiSPbPzbSTrMhTO8mrraixK1BpY6kfgHnFYqZ+8uK0zr0X17qNbt11X8WCKqrfjD9g1wfu8+uuD1ZImtOCcqp4Ql7gRqIRUyeORdTxd5M6/lZEbo+hnff49MBH0l1Q5tYZ2bUnKMuSThwrvdoK8bipNw5Nq+tYVG8cChfdifbZ0zGNXUpp2w6v3J7FvzZ5vDbd/gGfJsdNnXi79OYLKHdsf8CCTY6nNHYppS2333wv780EKu1KJqRoxJLXV/jlt+HL7SQr8thOci7DMNS61qXOt6Oamkyx1A/ArMnxlF59eVqxiKnb7/KpdU32y4qvp77JqQ/VO9R9OqZTx6P68Q+m1LbBrQ1bPHI4C/+aDFxtcjylMyejutCbkGVKTS1OtW10qyZ4/beygUqn1m1y63RnTGtuSd7w2GLTdSyqWMzS2g1udZ+K6ciLIe26118UWyZDUyl1vRNVQ7NDK1flbttoQ7NTa9e71H06rmDDwjtJACg8VipgwXrPxmSzSS05eIM7U6wxVCTFGqdDpux2ye1Zuj/WK1e7ZBhS3xKsVrAsS+OjSZ09GS26ApkA3td/Pq6XO6ZkmZb2PODPS6Aww2Y3tG6TRz/1c5VqWe3Sma6YfvT9SZ3vpuUtioNlWRq+mNBPXgzpxRemNHA+odVtLj3w0YB23lOxoJDgls0eebyG3n0zUjK/16MjSfWciattvVvb7vDqjrt9Gh1J6dA/hhSLFnYrh2VZevu1sOy2dEvIXHfM2rzdq8oqu46+GqaoLFBCSieyRUGlUpb6ehJqXOnMqrrv1QIr0p/MT02aqmtc9N0t2nQopQq/Le/tJOfyeG2qb3Lo/Lm4Nt7qkc2W28c2U5YuDSd18UJCFy8kFI1cfjN1NN1LesNWz2x7TwCFZVmWTh2P6tTxmKpr7dp5T4U83qXJ/WeWHa9e59K7b0Z09NWwzr1n1607vKqq5W3CQlmWpdCUqWTCUmWVXXaKYGbNTFm60JvQ2ZNRTU6YcnsMbbrVo9XrXHJl+B7E4UjXE3nrSFjnu+NatciaUPlmmpaOvR6Wx2do47Z0gcmW1S65XIZef2VaL/8wpLvvr1iy7ZpXO/deXKPDKd3+AW9eXqPsdkN37vbppf89pTd/Etbu+ytk5Pj9EYDc490CFmSgL6FE3NKqdbn51MzlNuR0GcWzUmHKlH/F0v+Bbl3r0mB/WMMXk2poXvwyv2TC0tDFhC72JTQ4kFAyIdnsUn1jehlhddCu3rNxnTsd04XzCbWscmr9Vo/8AcIFoFCSCUtvHQnr4oWEWte4dOtOb0FOSKtrHbq33a++cwmdOBbRwY6QWte6tPk2T072TC9HiYSlkcGEhi8mNTSQUCScDm9tNmlFtV3VtQ5VB9NfvT5+hjcTj5vqPRNX9+mYohFLgUqbtu/yauVq16KeEytXO3XuPbu63omqqdUlZxFv8Tl7MqapCVO77q24YitSfZNTuz/k15GD03rlhyHdfb9flVVL+7c7HErpxLGI6hodOVm1ej3+Sru27fDq7dcieq8rpvVb6N4BFDtCBSxI79m4fBU2Betz8ytjGIb8lbaiWIpvXW4n2VCAvXsNTU653IbOn4tnHSrEoubsaoSRwaRMU3K6DDW1uNS40qlgg+OKwlVbtnu1bqNbZ7pi6n4vpgu9Ca1cnV65UKhPPpBb8Zip4cGkbLb0G1E+MS1e06GUXnt5WlOTprbe7tHaDe4lXTF1tZl6L00tTp3qjOrsqZgG+uLasIUWlFJ6NcLkeEpDF5MaHkhodCQly5LsDqmuwalbNjvk9hgau5TS2EhS596L6eyp9G29PuNyyOBQda1dK6rsZf/znBGeTunsyZh6u9MdpoINDm3f5VZdoyMnzwfDMLRth1cH/09Ipzuj2rJ9cd0K8iUcSunk8agaV85fT6A66NA9D/j1kxdDeuVHU/rAfX7V1i3NW3nLsvT26xEZkrbvyv22h6u1rnVpeDCpk+9GVVvvKKl6GEA54hmKmwpNpXRpKKlNty6+QONcgUq7Ll4ofDuzSMSSuYTtJOey2Q2tXOVUz5m44jFzwcs6Q1OpdJDQl9DYpXQw46uwac0t7tkVCTfaTuH22LTldq/WbXLrvRMxnTsT04WehFrWuLR+i5twocRYpqXxsZSGBtKflo6Pvh/WOV2Gmludal3rUlWNvaAnrLjS8GBCbxwKS5Lu/mCF6hqLpyiZw2loy3avVrW5dPytiDrfThfIu+uDFWW3amEmpBseSGroYkKxaHo1QmWVXes2uVXX6FRN7ZUBQVNL+quZsjRxucjx2EhSo5eS6j+f/rtns0tV1fbZkKG61rFkW16KxdilpM6ejKm/LyFD0spV6eKLK6pz//a0qsah1rUunT0V06o2V9Gt0LMsS++8GZFhSNt2XD/0CKyw656fDugnL4b0kxdDunN3xZIUNOw9G9fIYFK37fQuyaobwzB0250+jV+a0puHp3X/hwNy5qADDoD8IFTATfWejcsw0qlxLvkrbYqftRSLmTmp05Ct6VD6BGypOj9crXWtW92n47rQm9Da9fPv9bQsSxOjKQ1cXpEQmkwXL1pRbdfGbR41rnQqsCLzmhBuj01b75gJF6LqORNX37m4Wte4tH6rW76K4nrThffFoubskuuhi0kl4ukTnaoauzZs9ai+yaFE3FLfubjOn4ur50xcFQGbWta41LLaJV8Fb84KxbIsdZ+Oq/NoRBUBmz5wb4UqiuwEZ4Y/YNddH/RroC+ut34STi+7/pB/Wf/+zA3phi8mNDaakqx0QFfX4FB9k0N1jc4FBQA2++XVCbUOaUP69T0SNjV2KamxkZTGLiXVfSqmM5fr0fkqbOmA4XLQUFl144C4FMXjpob6k+o5G9PocEoOp7Ruo1tr17vzfrK66VaPBs6nn3sfuM+f18fK1EBfQkMDSW293XPTn4OvwqZ7ftqvV1+a1uuvTGv7Lq9a1+avVkQkbKrzaETBeodWteVv28PVnC5DO3b79MoPQzr2ekQ7dud/hQSyZ5qW4jFLDodBF6EylHWosH//fvX19WnHjh16+OGHsz4Gxc00LZ3vjqu+OfefoMy0UQxNmnLXFTBUmGknWaBP51dUp984nu+OXxEqmClLI8NJDc4ptGgYUm2dQ2vWudWw0pmzN/Yer03bdvh0y2bPbLhw/lxcrWtdWr/Fs6xPIEqFaVoaH02lQ4SBpCbG0mGYy22oocmh+ian6hod16x2qW9yKpGwNHA+HRidfCeqk++kl5O2rHaqudVVdH/843FTl4aSujSUVDhsqnVNeivPcngzmUpZeueNiM53x9XQ7NAdd1cU9f7uGU0tLrk/ZNORl0J65YdTuvtD/mXVCjcaSYd0wxfnCem2uFXf6Eyv9MnBCb7XZ5PX51Jza/pyKmVpYiw1GzRcGk7qQm96NYPdnh5DOmRIBw2luFIkEn5/m96loaQsK70dZOvtHq1qcy/Za5DHa9P6rR6deDuqoYGE6puKY3VQIm7p3TcjWlFt15rrfLhwNbfbpt0f8uu1V6Z19NWIYjFLt2zKfe2BmW4PliVt3+Vd8tfh6lqHNt7qUdexqOoai7/Q5nJiWZYSCUvxqKVYzFI8ZioWTQcHsaiZvu7y11jUmn3dlNLd1Cr8NlUE7Je/2lTht6siYLtiS265ME1L0yFTUxMphSbTX/2VNm3cVpxbsbKRVai8eZqXAAAgAElEQVRw5MgRmaapffv26Rvf+IYGBgbU1NSU8TEofoP9CcVjllbn4UV8pvPA1ERqyfYEzmc6ZMpmlzzewr3IrVrr0rtvRTQ6nFQ0kn7zNVNo0W6X6prS+ysbmq49acylmXBh3aZ0uNB7Nh0urLocLlBobGmlT3TSIcLw4OUTHUOqrk2vUKlvcmhF9c23NDidhla1ubWqza3wdEp95xLqOxfX269F9M6bETW1ONWyxqW6ekdBqmwnk5ZGh5MaGUpqZDCpifH0J8N2e/qTqsELYVUEbFq30a2WNYsr2FZI0Yip11+Z1tillNZvcWvjttxuKcu3mqBDe34qoCMvhXToRyHd9cEKVdWU5oJH07Q0NpLS0OXn1+R4OqRzeww1NDtU3+hUsNGxJKvo7HZDNcHLe8Y3pt/IR8LW5ZAhqbFLKZ3pismyYpLSW/Vqgg7V1NlVW+eQb4k7Fy2EZVmamng/SPj/27uz2DiuM1/g/9p6qa5ubk1SpERSIkVKoiRS1k5p7NiOklwEudeYRA/GwMhDEAgIEiDjhwFmMHCsedCMMBPMALEdYxwgQTAvfolhXOAGN7iMYo1sybKsxZIoU6IlLuImiRSXrt67qu5DNSmSpiiy2Xv/fwBBNllsHun06arz1TnfNxcE1XwiWrbb2/RytR1rS6sTQ3dj6Lkahr9WzouVIL037KDAwefda2qPrAg4+LwH1y6G8OUXEcQiFnZ0pvd9ZXgghkfjCex6zg01Rzdftm53YuJBAjevhFHhl4sqoJlNlmXBMIDYomCAmQwS2CuH576ORe2fW0+p6qk4BDidAhwuAV6fBH+NAIdThNMpIJ6wEAyYCOr2jZC5LWNzXO6nBBw0EVKBBxzm8rTNzhgIzJrQZww7kBAwYS74v1Q1Ee4iu2GX0tVAT08Purq6AACdnZ3o7e39WsBgNcdQ/hu8G4PLLaB6Q/ovHN2qAElGzitABHUDHk9uL8rqmxT0fBHGJ2d0APbd57lEi9W1ctbfZN2qiN377JULfbciGOqPJUtxObB1B4MLmbLSRGfDRgU1G2T4N8hwrGNfqeqR0LZTQmu7E1OTBoYHYhgdimNkMA6XW8DGJnt7RCazihuGhelJAxMP7eSiU48NWCYgiMmAyU4XqmpkVFRKgACMD8fxVW8U1z8P4/bNCDa3OrE5hdJyuTT9OIFLHwcRj1nYd0RFfUP2lhCnU1mFhCMva/j0Ix0X/qLjwPNa2hL4ZpphWLhzawZ3+4KYeGAHbQUBqPBL2L7bDtL5ynOfd0QQBKgeAarHgY2N9uvESNhbMubyMoyPxnF/IAbAfn+oqpZRVS2jslpOaStcOlimhceTBsaH7UBCKGhfQVdUSdjRYW/Ty4cyxpIkoH2PG5c+DmKgL4rmbbmtLDA1mcDAVzFsaXWkFKSTJAF7D6twOMO4ezuKaNRE5wE1LcGSSNhEz9UIKqslbG7N3XuWIAh47pCKs38K4Mr5IP7qW96CDS4vJ5Gw7MnnrAnDsPN8mXOfzaWPF3zPtGAagGUChmkHAFY6znxKgACwk806nSIcTgFuVUB5hQKHay5wYAcLnC775w6nsKbXVyJuIagbCAZM6LqJYMD+enzEvnG5kEsVoCVXNMwHHrwiVI+YV31uWRbCQROB5KqD2RkDgRkTesCAuWBa41YFeMskVNcp8PokeMtEaD6pKFdrpHQlEI1GUVlZCQDQNA39/f0pHdPd3Y3u7m4AwOnTp+H3+1NpTk7JslyQ7V4NfTaOR+PT6NxfgZqaqoz8jfKKCKIRMaf/h9FwCBVVzme2IdN9ffCoA3oggaYtHlRvcOXF3RMAaGi0XwtfXJ5CX+8shu7FsG1nGTr2VUD1ZHcyEY+ZCATiiEVNWFbyBGpasKyvf7aSJ1PLWvD9hcdbFixzyc+Sz2lZwIAyCUkSICsiFIeY3CMoQlFEyIqQ/CxCUezvy7KQ0oV8UE9gZCiI4aEQRu+HEY+ZEASgps6F1u3l2NiootLvyMgkoboaaNtuT7buDwTxVW8A9+4Ecbc3iqpqJ1q2edHcqsGtrq+fTdPC5KMoxkbCGBsO4cFYBEZibjuPE7v2eFG3SUXtBhdk5euBgpoaYPdzFsZHwrhxdRq3b4Rw98soWtt92NlZDq9vfUuYMzm2DcPCvTsBXDg7A5dbwrd/UIeq6sJevuv3A9XVVfjT/x7BZ/8dxEv/YwMaNnty3awVzUzF8HH3OKYmZ+DRZDS3+rCpSUXdRjccztxPdFejdsOTry3LwsxUHOOjYTwYDWN8NIzR+2EAgMMporbOjQ31LtTWu1FV7czY+SSRMDF6P4yhezqGBoKIRkyIIlDfoGLPAQ8aNnuyfp5Y6Glju6rKwujQGPpuRdCxtw4ud25eA6Zp4ZPu+1A9Eo68uGldAeMXv23hi8opXP3sMQTE8eJ3NkCWU38+y7Lw5z+OwzSBF7+9EWXluQ+EvnDMg+7/M4b+28DhFxb3ayFcj1uWBT2QwOOJKKYmY5iajOLxZAyz0ysnLZckAaIk2J/FBY9Fcf77irLgmPlj5x5j/rEsi3C5JbhVCS73kw9lmXNvNkSjBgIzccxOxzE7/zmG8eE4otEnURBBADyaDF+5Al+ZgvuuKQiSCEW2Vzcoiv157lpt0WfZvm6TpLVfp1mWhaCewPTjGKYexzC94COReBIQUTUZFZVONDQ5UFHlQHmlA+UVjpJKLipYlmU9+7DFfve73+Ho0aNoa2vDxYsXMTIygu9///trPmap0dHRtTYl5/x+PyYmJnLdjIy4fTOMOz1RfPN7voztqb/6aRATDxP41v8qy8jzP4tlWfjjH2awZasT7XtW3tdUzH29WqGggb5bUdzvt5N3NrXYKxfSlW/DMCyEQyZCuolQcMGHbiIcMr8W0U4LARAF+4QlCJhf/m+ZWHTCWA1JBmRZSJ7YkAxKfP2xLAuIxy08Go8jMGOfNF1uATV1CmrqZPhrFCiO3ASVohETI0P29oiZKSMZ4LBrktfWr6485dzSZ3s7QxyTjxJIJK+ZvGV2aVp/rYKqaimlE+7stIG7tyMYGbSftL5BQcv21DPGp3tsJxJ2344Nx/Fg1L4jXlktYf+R4qqcEI2auHg2iNlpA3sOqdjUlPtJx3LuD8Rw43IIoijgG9/aAJcnlPPVCOk2d9ds8pGBx48SmHyUQFC331sk2d6XPreaobxKWtcdv1jUxIPRBMZH4ng0HodhALJil0jesElBzQYlb/K0rDS2A7MGzv7fABqbHejYr2a5Zba7vRHc+iKC/UdV1G1Kz/gZ+CqKG5fDqPRLOPi8J+VJzfCgnZy1vdOFlgzkakjVzath9N+J4sBfLa56kW/XaPG4hcC0fQd7dtr+CMwYSCSeHKNqInxlEnzlInzlErw+CbJiBw7EZADBvjbJj/GUbbGouXh1g24/DgVNJOL2TaG1kiRAkoUFn4Un127Sk++bpr2SemmfOV32ygOvT7Q/J78upeBBfX39st9P6QqsubkZvb29aGtrw+Dg4LJPvppjKH9ZpoWh/hiqN8gZTdKn+SQMD8YRj1s5SVYWCdtLwtQclJMsRKpHQucBFVt3ONF3K4qBr2IYvBfD5hYntu5wPnPCZJoWIuEngYKFgYNw0EQkvPgMIYr2VgxVE1FeqcDtsZfAORwCBNE+0QpzQYEFj4UFJ2JBwKIT8+KfP/1k7ff78ejRIxiGvfQ4kbCQiCe/Niz78zKPEwkLhmEfbyTsBFyRsIlE4snzmIbdhiq/jE2dDtRsSK16RyY4XSKa25xobnMiMGNvjxgejOHBaAiyAtQ3ONCw2YEK/5Nl4pZlIaSb8zkRJh4m5gNAqiaivsEBf60Mf42clkm1r1zCc4c82L7bxL07UQzdjWJkKA5/rWyX+KtNT237tZibaI2N2PuPTcPec1q30WFPtOryY+92OjmdIrpe0nDp4yCufhpCImatOslcNiQSFm5eDuP+QAyV1RL2HvagodGDiYlwrpuWdoIgQNUkqJo0X6kpEjbxeMJOePr4UQK3b0YA2O+H5ZUSKpNBhgq//MzzbyhoYHzEDiQ8fmQnWnS5BTRssbfpVVXLi0pqFgKvT8LmrQ7098XQ1OLISBnLlYSCJm7fjKC2Xk5rScjNW51wOARcuRjCJ2d0HP6GtubAfzRi4uaVMMorJTS35c+YBoAdHS5MPkzg2mchfOM73pxvx7SSCfgWBg9mZ+xrmjmyYp+3Nm22txb6kpPRfAm+5SuHU4TDKaJimQUofr8fDx88Sl5v2TeljPnP1vy12+KfLzhmwfGJhJ1HwjDM+d8VBHuOsmmzY1HwoJC2XWZbSu+gBw4cwJtvvompqSlcu3YNP//5z/H+++/j1Vdffeoxp06dSlujKfMejicQCVnYuSezd568ZXMVIAy75FaWBQO5LSdZqDyahD0HVbTucOLOrQju9UUxcDeKzVudaGp2IBazngQLFgQPwiFzcWRZANxuAapHRHWtMp+4Rk1+uNypbSlIF0GwVxjIsoB0XlbNbbvI94twb5mEHZ1ubN/twsTDBO4PxDAyGMPQvVgyWKAgGrYw8TCOcMju2LkcLNW1Mqpq0lehZDluVcTOPW60tbsweDeK/r4oLp4NwlcuomWbC/WNSkYn8pGwifHhOMYWZLR3uQU0bnGgbpOCyuriCyQspSgCDr3gweXzQdy4EkYsZqG13ZnzANnstIHLF4LQZ020tjvRtjN/tpRli8ttB/Tm8nfEYuZ8dYnJhwnc7Y3iqy+jgACUlUvJnAx2sMHhEDA7/STR4lx+F2+ZiK077ESLq0kSm+/adrkwMhTHzathHHlJy9q/x7Is3LwSAgDs2pv+Mon1jQ4oDgGXPgni4z/r6PrG2srW3rgchpGwsOegmpPkvSuRJAH7ulT89/8L4OrFELq+4claG2NRcz5oML/6YHbBHnoB0LwiKiolNDXbAQRvmQS3mttrmWIlJrd6KPm5SK7kpLT9AQB0Xcf169fR3t6O8vLylI9ZiNsf8selj4N4PJHAt/6nL6MTHz1g4C9/DGDPwczWWH6awbt28rfVbPEo1r5OBz1goK8nguGhOLDkHcXpEuaDBPMBAy35WBXz9kKf/b28RNzC2LC9PWLiYQKKQ7C3M9TI8NfK8Hhzt+LCMCyMDMZw93YU+qwJlyqguc2JpmeUrFtLX+sBOxHd2HAc04+fBCXrNik5zWifa6Zp4YvPQhgejKO5zYn2PbmpbGFZFobuxXDzahiKIuC5wyqqa/N3iXQuJRJ2hQl7u4Rd0nJucqQ4hPnycJV+CRs22q/vtUxM88Fq+ntuu0A2E6iODcfw+SeZ31owPZnAxXNBAMDhb3hWtRpj9H4Ml8+HsH23C63t+bPtYan7/VFc+yyMbbtdaGt3pWVsW5Z9xzqkmwjqJkJBe8n93MrKhVUMHE5hftWBrzy5FN4nFXz1gkLB9/LcSev2B8BOvnjkyJF1H0P5JxI28WA0juZtzozfSVU9IkQRCMyukJI2g4K6mVxiz5PAemheCc8d9qC13b4L5nLbgQO3Wpr1iIuZrNhLnhu2OBCPWZCV/NnvKUl26cyGLQ48HEvY+5WvRdDXE0XTVge2tDrXvAzYsizMThsYG45jfDg+/15VViFh224X6jYq0Hz5sXUll0RRwJ5DKhRHGPfuRBGPW+jYv7byeOuViFu4/nlofivM3sNqUeWwSDdZFlBdq8wHXQzDwsyU/R6uz9qlnmvrlaL/P2xqdmDwbhS3roVRW6dkfFIYj1u4eSUMX7mILRneWlBeJePoyxo+PWuXgT3wvAf+mqdvtYhGTdy4HEZZhYSW7fm17WGpTZsdeDSewJ2bETuovcocjYZh5x8Jzm3D1O3Sh3PbMo0lBclcqgCPJqGmTrH30ScDCU4XVx8QLVQYdaAoq+4PxGBZQGNz5iP2oijA4xVzVlYyGDDzssZ3odJ8Ul6UDKPsyFUyyWcRBAG19Qpq6xVMTSaXefdGce92FJuaHGje7lyxzvlcabyxZGm8cNAEBKDKL2Hnc25s2JjZbR2FShAE7HzODcUh4E6PHVjYe1jNShmwmakELp8PIRg0sW23C63bnXm3bDvfSZKASr+MSn9pXRoKooCdz6m48Bcdd29H0bYzs3fnb98IIxK2sP+oJytBN80n4eg3vfj0rI6LZ4PY2/X0pJA9V8KIxy10HUxPScpMEgQBu/epmJoM4MqFIJq2PLmOjEWTwYKFgYOgiZBuzG/VmyNKgCe5gtJfq8Cj2V97kqsr86mMIVE+K60zBz3T3NLRymoJWpaWOXp9EqanchRU0A14mKSRqGhVVMnYf1RGMGDg7u0o7g/EMNQfQ229jJbtLlT67fc5w7Aw8TCB8WQgIRa1IIqAv1ZGW7uzJO7YpoMgCNi2yw3FIaLnahifnQviwFFPxhKSWZaFga9iuHUtDIdTwJGXNFRV89KG1sZfI6OuQUHflxE0bHFkLPnf9OME+vti2LzVkdU8Um5VxNGXNXx2LojPz4fQsc9CU8vilQjjI3GMDMXRttMFX3lh3BxQHAL2dqn45M86/vjBCCzLQEg3EY8vDhzMbcOsrJbtoIFHmg8ecMUBUXrwzEuLTD5MIKSb2LYze+WVNJ+I0ftxGAkrq3vR7Nqz5qL9tkRUnDxeCR37VWzb5cLAV1H098Xw4IyOiioJ5ZUG7vfrSCTs8nvzpfHqlJxUpSkGzW1OKIqALy6FcOEjHYde8KQ9a3Y8ZuLapTDGh+OoqZOx55AKJzNzU4raO914MBrHl1+EsbfLk/bnN00L1z8Pw+kSsH33yiWsM8HhFHH4RQ2Xzwdx/fMwYlELW3fYSVVjMRPXPw/BVyaidUd+b3tYqqJKxu59bgzdM+BwCqioUubzNnk0CapHZJUFoixgUIEWGboXg6IIqNuUvYn23DJkPWCirCJ70fFoxC7tx8oPRKXD6RKxbZcbLdtduN8fw707UYzeD6G+wS796K+Vudw1TRq2OCArwJULIZz/S2ql7Z5majKByxdCiIRMtHe60Lwt9xUnqLCpHhFbtztxpyeKpq2JtK94GeiLYmbKwL4jas62jsmygAN/5cG1z0LovRFBNGph5x4Xbl2NIBa1cPB5T95XJVpOU4sT+w4xcR9RLjGoQPNiURNjw3E0tTiyumJgbg++PmtkNaigB+yEa9z+QFR6ZFnAllYntrQ6UVVVhcnJyVw3qSjVbXLg4AsCLn0cxCd/1nH4RQ88Wurv85Zl4d7tKL68HoHLLeDoyxoqSiwHAGVOy3aXXT3kShgvfEtLW16OcMhE780IaurkrN60WY4oCnjukAqHI4z+O1HMTtnVP7bucKK8kmOJiFLD2RTNGx6IwTSBxubsLn3zeEVAAAJZTtYY0pPl4BhUICppvMOdWdW1Crpe1BCPW/jkzzpmp1N7r49FTXx2LohbX0RQW6/ghe94GVCgtJJlAe2dbsxOG7g/EEvb8968EoZlAbv3ufPi/WYuqer23S5MPjKg+cSMJ6gkouLG2RQBsO/+DN6LobxSynqCHkkS4PGI0LNcVjKomxBEwJWhhExERGSrSJa2EwTg/F90TE0m1vT7k48SOPunACYeJLBrrxv7j6pwOPjeTelX36igwi/hy+sRxGPWs3/hGcZH7OSv23a6oHryJwGiIAhobXfh8IseHHpB47YvIloXnpEJADA1aUCfNbNSRnI5WpmY9ZUKwYAJ1SPmfdkkIqJi4C2TcPRlDYoi4MJHOh6Nx5/5O5Zloe9WBBf+okOUBBz9poYtrcyfQJkjCAJ2PedGLGrhzq3Iup4rEbdw43II3jIRzdvyMwFidS1L5BLR+vFdhADYCRolGdjYmJuggtcnIRgwYZrrvyuwWkHd5NYHIqIsUjUJR7+pQfWI+OxcEGPDT19iHo2Y+PRsEL03IqhrUPDCt73c801ZUV4po3GLA/13otDXccPj9s0IImELHftV3sAgoqLGGRUhHrcwOhTDxkZHzsruaD4JlmVP9LPBLidpMKhARJRlLreIIy9pKKuQ8Pn5EO73R792zKMHcZz9UwCPJxLo2O/G3sMqy3tSVm3vcEGSgZ5r4ZR+f2YqgXt9UTS1OFDJ3B9EVOQ4oyKMDMZgGMjZ1gcA8Prsl+J67gisRTRiwUjYteuJiCi7HE4Rh1/U4K+Rce2zMO7dtpeZm6aF3hthfPpREIoi4PljXjS1cLsDZZ/TJaKt3YWHYwk8GH32Vp2FLNPCF5fCcDoFbO9gAkQiKn4MnRKG7sXgKxNRXpm7CbaWnNwHZk3UZeHvza2I4EoFIqLckGUBB5/34OqnIfRciyActjD9OIHHjwxs2qxg9z4VchbLGxMttaXVicF7MfRcC6O6Voa4ymSGA1/FMDNlYG8XE4oSUWngO12Jm5lKYGbKQGOO7wTJigC3KkCfyc5Khflykl4OASKiXJEkAXu7VDRsceDe7ShmHhvYc1DFc4c8DChQzomSgJ173AgGTPR/9fVtOssJh0z03gijeoOM+gYlwy0kIsoPXKlQ4obuxSBKwMam3J/4NJ+EQJbKSuoBE4IAuFlOkogop0RRQOcBN/w1MsorJWg+bkuj/FFbr6CmTsadngg2NTngdK183dBzNQzTAnbvc3PbDhGVDM6oSlzbThf2H/HkxfI8r0+CHjBgWZmvABHSWU6SiChfCIKATZsdDChQXmrf44aRAHpvrFxi8sFoHGPDcbS1u+DR+FomotKR+5kk5ZTTJaK2PverFABA84kwDSAczPxqhaBuQmU+BSIiInoGr0/CllYnhu7FMP04sewxiYSFG5dD0HwiWrY5s9xCIqLc4qyK8obX9yRZYyaxnCQRERGtRdtOJxxOAT1Xw8uuqLxzM4JwyELHfnXVCR2JiIoFZ1WUN7QslZWMRS0k4iwnSURERKujOERs3+3C4wkDo/cXl5icmTJw704Ujc0OVFUzXRkRlR4GFShvOJwinC4h4ysVWE6SiIiI1qpxiwO+cgm3vggjkbBXK1imheufh6A4BOzocOW4hUREucFZFeUVzSdlfKVCMJAMKrCcJBEREa2SIArYtdeNSMjC3V47aePgvRimHxvYuccNh5PXFURUmvjuR3nF6xMRmM1sBYigbgACoLKcJBEREa1BVbWM+gYFX/VGMTWZwJfXw/DXynlRmpuIKFc4q6K8ovkkJOJANJK5oEJIN6GqIhMpERER0Zrt6HQDAM6f0WEawO59bggCrymIqHQxqEB5xZtM1hiYydwWCD1gcusDERERpUT1iNi63QXTBFrbXdCY+JmIShxT1FJe0ZJlJfVZE9Ub0v/8c+UkK6oc6X9yIiIiKgmtO5woq5BQU8dLaSIivhNSXnG6BCiKgECGkjXGY3Y5SZWVH4iIiChFoiRgw0bmUSAiArj9gfKMIAjQfGLGKkA8KSfJpYpERERERETrteaVCu+++y6Gh4exd+9e/OAHP1j2GMMw8LOf/Qy1tbUAgB/96EdobGxcX0upZHh9EsZH4xl5bpaTJCIiIiIiSp81BRUuXrwI0zRx6tQp/PrXv8bY2Bjq6uq+dtzg4CCOHj2K1157LW0NpdKhlYmI9VuIRk0401zzOajbKyBUD4MKRERERERE67WmoEJPTw+6uroAAJ2dnejt7V02qNDX14crV66gp6cHjY2NOHHiBCSJy81pdRYma3RWpzuoYMKtCpBYTpKIiIiIiGjdVgwqvPfeexgdHZ1/fOvWLbz88ssAAE3T0N/fv+zvtbS04I033kBFRQXefvttXL16Ffv37//acd3d3eju7gYAnD59Gn6/P+V/SK7IslyQ7c5nLkccnyEImG74/WVpfe5YJIzySldKfca+Li3s79LBvi4d7OvSwv4uHezr0sL+zj8rBhVOnDix6PHvfvc7xGIxAEAkEoFpmsv+XlNTExTFzojb3NyMsbGxZY87duwYjh07Nv94YmJi9S3PE36/vyDbnc8sy4IkAWMjM6iqTW9uhempGOoblJT6jH1dWtjfpYN9XTrY16WF/V062Nelhf2dO/X19ct+f01ry5ubm9Hb2wvAzptQU1Oz7HFvvfUWBgYGYJomLl26hKampjU2l0qZXQFCgh5YPmiVqljMRDxmwcNykkRERERERGmxptnVgQMHcO7cOfz+97/HhQsXsHfvXgwPD+P9999fdNzx48fx9ttv4+/+7u/Q1taGjo6OtDaaip/mExFIc1nJ0HzlB+b3ICIiIiIiSoc1JWpUVRVvvvkmrl+/jldeeQWqqkJVVbz66quLjmtsbMQvf/nLtDaUSovXJ2FkMI5E3IKspCepYlBPBhW4UoGIiIiIiCgt1hRUAOwEjUeOHMlEW4jmaT574q/PGiivWvPLdFlzQQWWkyQiIiIiIkoPzq4oL3nL7C0Kgdn05VUIBgy4VAGSzHKSRERERERE6cCgAuUl1SNCFO2VCukS1E14NOZTICIiIiIiShcGFSgviaIAjze9yRrtoAJf8kREREREROnCGRblLc0nQU/T9od4zEIsynKSRERERERE6cQZFuUtr09EMGjCMKx1P1dQt1c8eLx8yRMREREREaULZ1iUtzSfBFhAMLD+1QpPykkypwIREREREVG6MKhAecvrm6sAsf68CvPlJLn9gYiIiIiIKG04w6K85fGKgJCeChChgAmXW4DMcpJERERERERpw6AC5S1JEuDxiAjMpGP7g8FVCkRERERERGnGWRblNc0npmWlQlA3oTGfAhERERERUVoxqEB5zVsmQddNmGbqFSAScQvRiAWVlR+IiIiIiIjSirMsymuaV4JlAiE99S0Q8+Ukuf2BiIiIiIgorTjLorzmLbNfouupAPGknCRf7kREREREROnEWRblNc1r50HQZ9ezUmEuqMCcCkREREREROnEoALlNVkR4FaF9a1UCJhwugTICstJEhERERERpRODCpT3NJ+0zpUKBrc+EBERERERZQBnWpT3vD4JgVkDlpVaBYiQbnLrA4E8YwQAAAyjSURBVBERERERUQYwqEB5T/OJMA0gHFr7aoVEwkIkzHKSREREREREmcCZFuU9r89eZRCYWXtQYa4UpcbtD0RERERERGnHmRblPc1nv0z1FJI1BnX7d1QGFYiIiIiIiNKOMy3Kew6nCKdLQCCFZI3BAMtJEhERERERZQqDClQQ7AoQqaxUMOFwClAcLCdJRERERESUbgwqUEHw+sSUKkAEdZPlJImIiIiIiDKEsy0qCJpPQiIORCNrDCoEDHhY+YGIiIiIiCgjONuiguBNJmsMrGELhJEsJ8l8CkRERERERJmx5qDC9PQ0fvGLXzzzuHfffRf/+I//iD/84Q8pNYxoIS1ZVlJfQ7LGUHAuSSNjZ0RERERERJmwptmWrut45513EI1GVzzu4sWLME0Tp06dwoMHDzA2NrauRhI5XQIURUBgZvUrFYI6gwpERERERESZtKbZliiKeP311+F2u1c8rqenB11dXQCAzs5O9Pb2pt5CIgCCIEDzidADq1+pEAzYAQiVORWIiIiIiIgyQl7ph++99x5GR0fnH+/atQvHjx9/5pNGo1FUVlYCADRNQ39//7LHdXd3o7u7GwBw+vRp+P3+VTc8X8iyXJDtLkT+GhP3B4Kr/v++k3gIpyuG+vqatPx99nVpYX+XDvZ16WBflxb2d+lgX5cW9nf+WTGocOLEiZSe1OVyIRaLAQAikQhMc/m7y8eOHcOxY8fmH09MTKT093LJ7/cXZLsLkeKIIRI2MDryEA7ns1cfTE6E4FaFtPUP+7q0sL9LB/u6dLCvSwv7u3Swr0sL+zt36uvrl/1+RtaFNzc3z295GBwcRE1Neu4UU2nTyuxkjYFVJmtkOUkiIiIiIqLMWveMa3h4GO+///6i7x04cADnzp3D73//e1y4cAF79+5d758hmi8rqa+irKRhWAiHWE6SiIiIiIgok1bc/vA0J0+enP9606ZNePXVVxf9XFVVvPnmm7h+/TpeeeUVqKq6rkYSAYBbFSFJq1upwHKSREREREREmZdSUGE1NE3DkSNHMvX0VILsChDSqlYqBJNVIrj9gYiIiIiIKHM446KCovlEBFYTVNDtY7hSgYiIiIiIKHM446KC4vVJiIQsJOLWiseFdBOKIkBxCFlqGRERERERUelhUIEKirbKZI16wITHK0IQGFQgIiIiIiLKFAYVqKB4fasrKxnSTW59ICIiIiIiyjDOuqigqJoIQVx5pYJpWAiFTKgMKhAREREREWUUZ11UUERRgKatnKwxFDIBC/BoUhZbRkREREREVHoYVKCCo5VJ0FfY/sBykkRERERERNnBWRcVHK9PRDBowjCWrwAR1JNBBW5/ICIiIiIiyijOuqjgaD4JsJ6sSFgqGDAgK4DDycoPREREREREmcSgAhWcJxUgls+rENRNeDSJ5SSJiIiIiIgyjEEFKjgerwgIT68AwXKSRERERERE2cGZFxUcSRLg8YgILJOs0TQthIImkzQSERERERFlAWdeVJA0nwh95usrFcJBE5bFJI1ERERERETZwJkXFSSvT4KumzDNxRUg5io/qJqUi2YRERERERGVFAYVqCBpPgmWaedPWIjlJImIiIiIiLKHMy8qSF6f/dJdWgEiGDAgyYDTxcoPREREREREmcagAhUkLVlWUp/9+koFlpMkIiIiIiLKDgYVqCDJigCXKnx9pQLLSRIREREREWUNZ19UsLw+adFKBZaTJCIiIiIiyi7OvqhgaT4J+qwBy7IrQIRDJiyTSRqJiIiIiIiyhbMvKlhenwjDsIMJwMLKDywnSURERERElA0MKlDBmkvWGEhugQgFkkEFbn8gIiIiIiLKCs6+qGDNlZXUZ+xkjUHdhCixnCQREREREVG2MKhABcvhFOFwCvPJGoO6AY8mspwkERERERFRljCoQAXNWybNl5UMBkx4vMynQERERERElC1rDipMT0/jF7/4xYrHGIaBn/zkJzh58iROnjyJoaGhlBtItBLNK0KfNWHNlZNk5QciIiIiIqKskddysK7reOeddxCNRlc8bnBwEEePHsVrr722rsYRPYu3TEI8HsP0YwMmy0kSERERERFl1ZpmYKIo4vXXX4fb7V7xuL6+Ply5cgX/8A//gHfffReGYayrkURPoyWTNY6PxgEwqEBERERERJRNK65UeO+99zA6Ojr/eNeuXTh+/Pgzn7SlpQVvvPEGKioq8Pbbb+Pq1avYv3//+ltLtIQ3WVbywVxQgTkViIiIiIiIsmbFoMKJEydSetKmpiYoigIAaG5uxtjY2LLHdXd3o7u7GwBw+vRp+P3+lP5eLsmyXJDtLhaWZcHh0BGYMSFJAjY1VGes+gP7urSwv0sH+7p0sK9LC/u7dLCvSwv7O/+sKafCar311lv4/ve/j8bGRly6dAl//dd/vexxx44dw7Fjx+YfT0xMZKI5GeX3+wuy3cXE4xUQmwRUj4DJycmM/R32dWlhf5cO9nXpYF+XFvZ36WBflxb2d+7U19cv+/11BxWGh4fx8ccf49VXX53/3vHjx/GrX/0KlmVh//796OjoWO+fIXoqzSdhatKA6mU+BSIiIiIiomxKKahw8uTJ+a83bdq0KKAAAI2NjfjlL3+5roYRrZY3mazRozGfAhERERERUTbx1i4VPC2ZrJGVH4iIiIiIiLKLszAqeJV+GTV1Mmo2ZCRFCBERERERET0FZ2FU8BSHgEMvaLluBhERERERUcnhSgUiIiIiIiIiSgmDCkRERERERESUEgYViIiIiIiIiCglDCoQERERERERUUoYVCAiIiIiIiKilDCoQEREREREREQpYVCBiIiIiIiIiFIiWJZl5boRRERERERERFR4uFJhnf7+7/8+102gLGFflxb2d+lgX5cO9nVpYX+XDvZ1aWF/5x8GFYiIiIiIiIgoJQwqEBEREREREVFKpJMnT57MdSMKXXNzc66bQFnCvi4t7O/Swb4uHezr0sL+Lh3s69LC/s4vTNRIRERERERERCnh9gciIiIiIioouq7j+vXrmJ2dzXVTKMPY1/mP2x/W4d1338WHH36I6elptLe357o5lCGGYeCnP/0pPv/8c3z00Udobm5GWVlZrptFaTY9PY1//ud/xksvvQSA47vYLexvjvHiFQqF8G//9m84e/YsLl68iEOHDuE///M/ObaL0HJ9zXFdvHRdx+nTp+F0OvFf//Vf6Orqwm9/+1uO7SK0XF//7d/+Lcd2npFz3YBCdfHiRZimiVOnTuHXv/41xsbGUFdXl+tmUQYMDg7i6NGjeO2113LdFMoQXdfxzjvvIBqNAuD4LnZL+5tjvHidO3cO3/ve99DR0YHf/OY3+OSTTzi2i9TSvv7www85rovY0NAQfvjDH6KtrQ26ruPmzZsc20VqaV+fOXOGYzsPMaiQop6eHnR1dQEAOjs70dvbyzevItXX14crV66gp6cHjY2NOHHiBCRJynWzKI1EUcTrr7+Of/3XfwXA8V3slvY3x3jx+s53vjP/9ezsLM6dO4fvfve7ADi2i83Svq6qquK4LmJzKxFu3bqFu3fvQtd1nreL1NK+PnToEMd2HmJOhRRFo1FUVlYCADRNw8zMTI5bRJnS0tKCN954A//yL/8CwzBw9erVXDeJ0kxVVaiqOv+Y47u4Le1vjvHid+fOHQSDQVRVVXFsF7m5vu7o6OC4LnKWZeH8+fPweDwQBIFju4gt7OstW7ZwbOchBhVS5HK5EIvFAACRSASmaea4RZQpTU1NqKioAGCXrxkbG8txiyjTOL5LC8d4cdN1Hb/97W/xk5/8hGO7yC3sa47r4icIAn784x+jsbERd+7c4dguYgv7empqimM7DzGokKLm5mb09vYCsPfj1tTU5LhFlClvvfUWBgYGYJomLl26hKamplw3iTKM47u0cIwXr0QigX//93/H3/zN36C6uppju4gt7WuO6+L24Ycf4uzZswDsJJ2vvPIKx3aRWtrXv/nNbzi28xBzKqTowIEDePPNNzE1NYVr167h1KlTuW4SZcjx48fxq1/9CpZlYf/+/ejo6Mh1kyjDOL5LC8d48Tpz5gz6+/vxwQcf4IMPPsCLL76Ic+fOcWwXoaV9vXPnTrz99tsc10Xq2LFj+I//+A+cOXMGDQ0NOHjwIM/bRWppX//TP/0Tz9l5SLAsy8p1IwrVXM3U9vZ2lJeX57o5RJRGHN9ExYljm6g4cWwT5Q6DCkRERERERESUEuZUICIiIiIiIqKUMKhARERERERERClhUIGIiIiIiIiIUsKgAhERERERERGlhEEFIiIiIiIiIkoJgwpERERERERElJL/D7uAdrOUnM7KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#平稳性检验\n",
    "# 时序图\n",
    "# 变量：总出险数量\n",
    "df_1b[\"diff1\"] = df_1b[col_1b].diff(1).dropna()\n",
    "df_1b[\"diff2\"] = df_1b[\"diff1\"].diff(1).dropna()\n",
    "data1 = df_1b.loc[:,[col_1b,\"diff1\",\"diff2\"]]\n",
    "data1.plot(subplots=True, figsize=(18, 12),title=\"总出险数量差分图\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c572e5f9-a687-49b4-80f6-86b7f3cc4ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单位根检验:\n",
      "\n",
      "   Augmented Dickey-Fuller Results   \n",
      "=====================================\n",
      "Test Statistic                 -5.302\n",
      "P-value                         0.000\n",
      "Lags                                5\n",
      "-------------------------------------\n",
      "\n",
      "Trend: Constant\n",
      "Critical Values: -3.66 (1%), -2.96 (5%), -2.62 (10%)\n",
      "Null Hypothesis: The process contains a unit root.\n",
      "Alternative Hypothesis: The process is weakly stationary.\n"
     ]
    }
   ],
   "source": [
    "# 单位根检验\n",
    "print(\"单位根检验:\\n\")\n",
    "print(ADF(df_1a.diff1.dropna()))  \n",
    "# Lags   表示滞后期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e4f65617-05bb-4454-8587-8a2652725927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单位根检验:\n",
      "\n",
      "   Augmented Dickey-Fuller Results   \n",
      "=====================================\n",
      "Test Statistic                 -8.298\n",
      "P-value                         0.000\n",
      "Lags                                2\n",
      "-------------------------------------\n",
      "\n",
      "Trend: Constant\n",
      "Critical Values: -3.64 (1%), -2.95 (5%), -2.61 (10%)\n",
      "Null Hypothesis: The process contains a unit root.\n",
      "Alternative Hypothesis: The process is weakly stationary.\n"
     ]
    }
   ],
   "source": [
    "# 单位根检验\n",
    "print(\"单位根检验:\\n\")\n",
    "print(ADF(df_1b.diff1.dropna()))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35dc61b5-8ba7-43fa-b9db-7745e5fdb3e2",
   "metadata": {},
   "source": [
    "### 确定ARMA的阶数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "373821c1-cff6-40ee-999f-ab94a89cf0fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 利用自相关图和偏自相关图\n",
    "####自相关图ACF和偏相关图PACF\n",
    "\n",
    "def acf_pacf_plot(ts_log_diff):\n",
    "    sm.graphics.tsa.plot_acf(ts_log_diff,lags=10) #ARIMA,q\n",
    "    sm.graphics.tsa.plot_pacf(ts_log_diff,lags=10) #ARIMA,p\n",
    "    \n",
    "acf_pacf_plot(df_1a[col_1a])   #查看数据的自相关图和偏自相关图\n",
    "\n",
    "# 暂定 p=1, q=2\n",
    "# 自相关图拖尾，或者 1 阶截尾\n",
    "# 偏相关图 2 阶截尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "91d99464-53a4-4ab3-af7d-ab776bc34897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总出险数量\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(col_1b)\n",
    "acf_pacf_plot(df_1b[col_1b])   #查看数据的自相关图和偏自相关图\n",
    "\n",
    "# 暂定 p=3, q=3\n",
    "# 自相关图拖尾，或者 3 阶截尾\n",
    "# 偏相关图 3 阶截尾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9f088039-52f4-4857-838c-c3c2f8490ee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.总理赔金额   No. Observations:                   37\n",
      "Model:                 ARIMA(1, 1, 2)   Log Likelihood                  23.522\n",
      "Method:                       css-mle   S.D. of innovations              0.118\n",
      "Date:                Sun, 06 Nov 2022   AIC                            -37.044\n",
      "Time:                        21:42:32   BIC                            -28.990\n",
      "Sample:                             1   HQIC                           -34.205\n",
      "                                                                              \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const             0.0132      0.046      0.285      0.775      -0.077       0.104\n",
      "ar.L1.D.总理赔金额    -0.3246      0.305     -1.065      0.287      -0.922       0.273\n",
      "ma.L1.D.总理赔金额     1.1827      0.152      7.803      0.000       0.886       1.480\n",
      "ma.L2.D.总理赔金额     0.9999      0.107      9.361      0.000       0.791       1.209\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -3.0811           +0.0000j            3.0811            0.5000\n",
      "MA.1           -0.5914           -0.8064j            1.0001           -0.3507\n",
      "MA.2           -0.5914           +0.8064j            1.0001            0.3507\n",
      "-----------------------------------------------------------------------------\n",
      "[0.06116 0.07914]\n"
     ]
    }
   ],
   "source": [
    "# 对于总理赔金额，p=1, q=2, d=1\n",
    "p, q, d = 1, 2, 1\n",
    "\n",
    "model_1a = ARIMA(df_1a[col_1a], order=(p, d, q))\n",
    "result = model_1a.fit()\n",
    "print(result.summary())\n",
    "############################预测未来走势##########################################\n",
    "# forecast方法会自动进行差分还原，当然仅限于支持的1阶和2阶差分\n",
    "forecast_n = 2 #预测未来2个数据\n",
    "forecast = result.forecast(forecast_n)\n",
    "forecast = forecast[0]\n",
    "print (forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "78e44ecb-cfa0-40fa-88cc-013097244d27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                D.总出险数量   No. Observations:                   37\n",
      "Model:                 ARIMA(3, 1, 3)   Log Likelihood                  15.325\n",
      "Method:                       css-mle   S.D. of innovations              0.145\n",
      "Date:                Sun, 06 Nov 2022   AIC                            -14.650\n",
      "Time:                        21:56:51   BIC                             -1.763\n",
      "Sample:                             1   HQIC                           -10.107\n",
      "                                                                              \n",
      "=================================================================================\n",
      "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const            -0.0027      0.003     -1.065      0.287      -0.008       0.002\n",
      "ar.L1.D.总出险数量    -1.0156      0.190     -5.339      0.000      -1.388      -0.643\n",
      "ar.L2.D.总出险数量    -0.6916      0.218     -3.168      0.002      -1.120      -0.264\n",
      "ar.L3.D.总出险数量    -0.0836      0.225     -0.372      0.710      -0.524       0.357\n",
      "ma.L1.D.总出险数量     0.2386      0.082      2.898      0.004       0.077       0.400\n",
      "ma.L2.D.总出险数量    -0.2384      0.130     -1.841      0.066      -0.492       0.015\n",
      "ma.L3.D.总出险数量    -0.9999        nan        nan        nan         nan         nan\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -0.7702           -1.0879j            1.3330           -0.3480\n",
      "AR.2           -0.7702           +1.0879j            1.3330            0.3480\n",
      "AR.3           -6.7294           -0.0000j            6.7294           -0.5000\n",
      "MA.1           -0.6192           -0.7852j            1.0000           -0.3563\n",
      "MA.2           -0.6192           +0.7852j            1.0000            0.3563\n",
      "MA.3            1.0001           -0.0000j            1.0001           -0.0000\n",
      "-----------------------------------------------------------------------------\n",
      "[0.18962 0.17669]\n",
      "逆变换\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "non-broadcastable output operand with shape (2,1) doesn't match the broadcast shape (2,15)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-e1ad48a9dbed>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mforecast\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'逆变换'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mforecast\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnewshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\lenovo\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36minverse_transform\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    459\u001b[0m                         force_all_finite=\"allow-nan\")\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 461\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    462\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscale_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    463\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: non-broadcastable output operand with shape (2,1) doesn't match the broadcast shape (2,15)"
     ]
    }
   ],
   "source": [
    "# 对于总出险数量，p=3, q=3, d=1\n",
    "p, q, d = 3, 3, 1\n",
    "\n",
    "model_1b = ARIMA(df_1b[col_1b], order=(p, d, q))\n",
    "result = model_1b.fit()\n",
    "print(result.summary())\n",
    "############################预测未来走势##########################################\n",
    "# forecast方法会自动进行差分还原，当然仅限于支持的1阶和2阶差分\n",
    "forecast_n = 2 #预测未来2个数据\n",
    "forecast = result.forecast(forecast_n)\n",
    "forecast = forecast[0]\n",
    "print (forecast)\n",
    "print('逆变换')\n",
    "scaler.inverse_transform(np.reshape(forecast, newshape=(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eb1eb5f-ac88-45aa-8cdf-aebf6184b4ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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